Electrical Equivalent Circuit Parameter Estimation of Commercial Induction Machines Using an Enhanced Grey Wolf...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Computational Intelligence

Background:

• Induction motors are critical industrial components with high energy consumption.
• Accurate parameter identification is essential for optimizing motor efficiency but is a complex, nonlinear optimization problem.
• Traditional algorithms struggle with multimodal error surfaces and balancing exploration/exploitation, leading to suboptimal parameter estimation.

Purpose of the Study:

• To develop an enhanced optimization algorithm for accurate induction motor parameter estimation.
• To improve the energy efficiency of induction motors through precise parameterization.
• To address the limitations of existing optimization techniques in handling complex, multimodal problems.

Main Methods:

• Introduction of the Adaptive Weight Grey Wolf Optimizer (AWGWO).
• AWGWO features an adaptive weight mechanism to dynamically balance exploration and exploitation phases.
• Validation through extensive simulations on various induction motor models, including eight commercial motors.

Main Results:

• AWGWO demonstrated superior performance compared to state-of-the-art algorithms in convergence speed and solution accuracy.
• The adaptive mechanism effectively mitigated premature convergence to local optima.
• AWGWO achieved higher accuracy and robustness in parameter estimation across multimodal landscapes.

Conclusions:

• AWGWO provides a more effective and reliable method for induction motor parameter identification.
• The algorithm's enhanced balance between exploration and exploitation leads to significant improvements in accuracy.
• This work offers a valuable tool for enhancing induction motor energy efficiency in industrial applications.